Titlebook: Brain-Computer Interfaces; Revolutionizing Huma Bernhard Graimann,Gert Pfurtscheller,Brendan Allis Book 2010 Springer-Verlag GmbH Germany,

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书目名称Brain-Computer Interfaces影响因子(影响力)




书目名称Brain-Computer Interfaces影响因子(影响力)学科排名




书目名称Brain-Computer Interfaces网络公开度




书目名称Brain-Computer Interfaces网络公开度学科排名




书目名称Brain-Computer Interfaces被引频次




书目名称Brain-Computer Interfaces被引频次学科排名




书目名称Brain-Computer Interfaces年度引用




书目名称Brain-Computer Interfaces年度引用学科排名




书目名称Brain-Computer Interfaces读者反馈




书目名称Brain-Computer Interfaces读者反馈学科排名

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,Brain Signals for Brain–Computer Interfaces,principles, and outlines a set of brain signals appropriate for BCI use. Section 2 describes specific brain signals used in BCIs, their neurophysiological origins, and their current applications. Finally, Sect. 3 discusses issues critical for maximizing the effectiveness of BCIs.

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Dynamics of Sensorimotor Oscillations in a Motor Task,re, many people can use BCI systems by imagining movements to convey information. The EEG has many regular rhythms. The most famous are the occipital alpha rhythm and the central mu and beta rhythms. People can desynchronize the alpha rhythm (that is, produce weaker alpha activity) by being alert, a

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Neurofeedback Training for BCI Control,or external devices (for a comprehensive review, see [1]). BCIs typically measure electrical signals resulting from neural firing (i.e. neuronal action potentials, Electroencephalogram (ECoG), or Electroencephalogram (EEG)). Sophisticated pattern recognition and classification algorithms convert neu

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The Graz Brain-Computer Interface,[36, 38] of single-trial electroencephalographic (EEG) data during actual (overt) and imagined (covert) hand movement [9, 18, 40]. At the beginning of our BCI research activities we had a cooperation with the Wadsworth Center in Albany, New York State, USA, with the common interest to control one-di

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BCIs in the Laboratory and at Home: The Wadsworth Research Program, and control. Numerous studies over the past two decades have indicated that scalp-recorded electroencephalographic (EEG) activity can be the basis for non-muscular communication and control systems, commonly called brain–computer interfaces (BCIs) [55]. EEG-based BCI systems measure specific featur

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,Detecting Mental States by Machine Learning Techniques: The Berlin Brain–Computer Interface,ized for revealing the user’s mental state. Classical BCI applications are brain actuated tools for patients such as prostheses (see Section 4.1) or mental text entry systems ([1] and see [2–5] for an overview on BCI). In these applications, the BBCI uses natural motor skills of the users and specif

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